Multiomics insights on the onset, progression, and metastatic evolution of breast cancer

Breast cancer is the most common malignant neoplasm in women. Despite progress to date, 700,000 women worldwide died of this disease in 2020. Apparently, the prognostic markers currently used in the clinic are not sufficient to determine the most appropriate treatment. For this reason, great efforts have been made in recent years to identify new molecular biomarkers that will allow more precise and personalized therapeutic decisions in both primary and recurrent breast cancers. These molecular biomarkers include genetic and post-transcriptional alterations, changes in protein expression, as well as metabolic, immunological or microbial changes identified by multiple omics technologies (e.g., genomics, epigenomics, transcriptomics, proteomics, glycomics, metabolomics, lipidomics, immunomics and microbiomics). This review summarizes studies based on omics analysis that have identified new biomarkers for diagnosis, patient stratification, differentiation between stages of tumor development (initiation, progression, and metastasis/recurrence), and their relevance for treatment selection. Furthermore, this review highlights the importance of clinical trials based on multiomics studies and the need to advance in this direction in order to establish personalized therapies and prolong disease-free survival of these patients in the future.


Introduction
According to GLOBOCAN 2020, female breast cancer (BC) is the most common type of cancer.In 2020, female BC accounted for nearly 700,000 cancer deaths and 2.3 million new cases worldwide (1).Breast cancer is highly heterogeneous, and its treatment classically depends on its clinical status (early stage, locally advanced or metastatic), histological characteristics and biomarker profile (2).Histologically, breast cancers can be classified as sarcomas or carcinomas depending on whether they originate from connective tissue or epithelial cells.In turn, carcinomas are classified as carcinomas in situ if they have not invaded other tissues, or as invasive carcinomas if they have invaded adjacent tissues or other organs of the body.Both in situ and invasive carcinomas are found in the lobules and ducts (lobular carcinoma in situ, LCIS; ductal carcinoma in situ, DCIS; invasive lobular carcinoma, ILC; and invasive ductal carcinoma, IDC) (2).The presence or absence of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) molecularly determines breast cancer subtypes (3).Estrogen receptor positive (ER+) tumors require estrogen to subsist and grow.PR expression, in turn, is estrogen dependent.Therefore, ER+ and/or PR+ tumors are amenable to endocrine therapies targeting estrogen biosynthesis or estrogen receptors (4,5).Likewise, HER2 amplification or overexpression determines the use of HER2-targeted therapy (6).Breast cancer can be classified into luminal A, luminal B, HER2-enriched and triple-negative breast cancer (TNBC), also known as basal-like.The luminal A subtype is characterized as ER+ and/or PR+ but HER2−, the luminal B subtype is ER+ and/or PR+/HER2+, the HER2enriched subtype is characterized by overexpression of HER2 and ER−/PR−, and TNBC is negative for ER, PR and HER2 expression (2,3,7,8).In addition, six TNBC subtypes have been identified: basal-like 1 (BL1), basal-like 2 (BL2), mesenchymal (M), mesenchymal stem-like (MSL), immunomodulatory (IM), and luminal androgen receptor (LAR) (9).In principle, the stratification of patients allows the establishment of treatments adapted to each subtype of breast cancer (2, 10) (Figure 1).
Despite efforts, 700,000 women died from breast cancer in 2020 (1).Apparently, prognostic markers currently used in the clinic, such as ER and HER2 status, histologic subtype, size, lymph node status and Nottingham grade, as well as current predictive tests such as germline BRCA status, tumor PIK3CA mutation status and programmed death-ligand 1 (PDL-1) status, are not sufficient to determine the most appropriate treatment (11).Relapses are the main obstacle faced by clinicians and are mainly due to the development of resistance to the drugs administered.However, there are still insufficient data to determine whether resistance develops after exposure to the drug or whether resistant cells are present from the onset of the disease, preceding antineoplastic treatments (12).This has prompted the search for biomarkers related to different characteristics of breast cancers, such as genetic alterations, epigenetic reprogramming, tumor-promoting inflammation and immune evasion, dysregulation of cell metabolism, or changes in the microbiota, among others (13,14).Over the past 50 years, efforts have been made to identify genetic and post-transcriptional alterations, changes in protein expression, and more recently, metabolic changes and even immunological or microbial changes.These changes can be detected at the single molecule or pathway level and can serve as markers for diagnosis and/or discovery of personalized therapies.The detection of these modifications is made possible by technologies known as "omics", such as genomics, transcriptomics, proteomics or metabolomics, or more specific ones such as epigenomics, glycomics or lipidomics, among others.The identification of new biomarkers might allow Schematic of the workflow performed in the review.Once the commonly accepted classifications of breast cancer have been described, the biomarkers identified by different omics techniques are described.more precise, and personalized therapeutic decisions in both primary and recurrent cancers (Figure 1).
Genomics collectively characterizes and quantifies all the genes of an organism.Genomic analysis includes techniques such as microarrays, gene expression profiling, serial analysis of gene expression (SAGE), comparative genomic hybridization (CGH), array-based CGH (array-CGH), whole genome amplification (WGA), and high-throughput sequencing or next-generation sequencing (NGS).Different genomic studies have focused on the search for markers with predictive or prognostic value, for patient stratification and for determining the appropriate therapy (15,16).Transcriptomics studies the set of all RNA transcripts of an organism.The most commonly used techniques to study the transcriptome are microarrays and RNA sequencing (RNA-seq) using high-throughput sequencing or NGS.Transcriptomic analysis allows the measurement of differential gene expression, often presented as hierarchical clustering (17,18).Proteomics detects and quantifies the presence of proteins produced or modified by an organism.Proteomic analysis is performed by using separation techniques (gas chromatography, liquid chromatography, ultrahigh performance l iquid c hr oma tograp hy, capillary electrophoresis) combined with mass spectrometry (MS, resulting in GC-MS, LC-MS, UPLC-MS, CE-MS, respectively), nuclear magnetic resonance spectroscopy (NMR), reverse phase protein arrays (RPPA), and sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS) (19).Glycomics provides insight into the biological significance of Nglycosylation of plasma proteins in cancer (20).Metabolomics approaches based on NMR, LC-MS, GC-MS and desorption electrospray ionization mass spectrometry imaging (DESI-MSI) enable the linking of genotype and phenotype thanks to the knowledge generated by dynamic metabolism (21,22).Epigenomics (study of gene modifications through the aggregation of chemical compounds, with no changes in the DNA sequence), lipidomics (study of lipids in the biological system), and microbiomics (study of microorganisms present in the human body) are branches that use some of the mentioned above.
Due to the complexity that governs carcinogenesis and tumor progression, recent years have seen efforts to integrate data from different omics into a computational approach that allows for more complex reconstruction of biochemical connections (23).This review aims to summarize the existing knowledge on the advances made thanks to omics studies in human breast cancer.In particular, we focus on 1) the discovery of markers that can be used for diagnosis, 2) molecular and/or signaling pathway alterations in onset, progression, and metastasis, and 3) resistance to therapies and attempts to establish personalized treatments (Figure 1).

Implication of omics in the identification of cancer-specific and prognostic biomarkers
A cancer biomarker, found in tissues or body fluids, is used to detect the presence of cancer (differences between tumor and healthy samples or between tumor subtypes) and provides information on prognosis/prediction, cancer progression, and cancer medicine/treatment guidance, among others (24).In this review, we provide an overview of cancer-specific molecules and/or pathways that have been identified as biomarkers in breast cancer using different omics technologies.

Diagnostic biomarkers
One of the first uses of omics has been to identify biomarkers that differentiate tumor tissue from healthy tissue, as well as to identify biomarkers specific to different subtypes of breast cancer.In addition, the last decade has seen efforts to develop minimally invasive techniques to predict breast cancer subtypes, such as identifying biomarkers in human plasma, saliva, and urine.
Eight pathways have been implicated in breast cancer: protein digestion and absorption, central carbon metabolism in cancer, neuroactive ligand receptor interaction, ABC transporters, mineral uptake, inositol phosphate metabolism, glutathione metabolism, and cysteine and methionine metabolism (49).A combinatorial study of metabolomic and proteomic profiles in human plasma samples has identified metabolic signatures for breast cancer diagnosis.The most abundant metabolites in breast cancer patients include mainly primary and secondary compounds of bile acid metabolism, compounds of fructose, mannose, galactose, tyrosine and glycerolipid metabolism.Critical metabolic pathways in breast cancer include alanine, aspartate and glutamate pathways, glutamine and glutamate metabolic pathways, and arginine biosynthesis pathways.The specific metabolites are listed in Table 1.In addition, thanks to the combined study with the proteome, aspartate aminotransferase (GOT1), L-lactate dehydrogenase B-chain (LDHB), glutathione synthetase (GSS) and glutathione peroxidase 3 (GPX3) have been found to be closely involved in these metabolic pathways (34).
On the other hand, metabolomic analysis of polyamines in saliva before and after surgical treatment has shown a decrease in N1-Ac-SPD and an increase in N8-Ac-SPD in patients after surgical treatment.Thus, the ratio (N8-Ac-SPD)/(N1-Ac-SPD+N8-Ac-SPD) could be an index of health status after surgical treatment (32) (Table 1).A metabolomic study of urine and breast tissue samples has identified dysregulation of lactate, valine, aspartate and glutamine pathways in breast cancer.In addition, five metabolites (acetone, 3-hexanone, 4-heptanone, 2-methyl-5-(methylthio)furan, and acetate) allow correlation between urine and tissue samples (33).Inositol triphosphate receptor (IP3R) type 2 and 3 expression is increased in breast tumor tissue compared to adjacent healthy tissue.Increased lipoproteins, increased levels of metabolites such as lactate, lysine and alanine, and decreased levels of pyruvate and glucose in the serum of patients with high IP3R expression compared to healthy individuals (35).
The combination of metabolic profiling with tissue protein expression increases the accuracy in characterizing breast cancer patients (35).Lipidomic analyses have shown that the levels of sphingosine-1-phosphate (S1P), ceramides, and other sphingolipids are significantly higher in breast tumors than in normal breast tissue (36).In addition, sphingomyelin phosphodiesterase (SMase), which converts sphingomyelin to ceramide phosphate, is downregulated in 60% of breast tumors (37).An increase in fatty acids such as palmitate-containing phosphatidylcholines (PC) has also been found, especially in ER− and grade 3 tumors compared to healthy breast tissue.Phospholipids may have diagnostic potential as they have been associated with cancer progression and patient survival (38).When plasma samples from breast cancer patients and healthy controls were compared, significantly lower levels of lysophosphatidylcholines (LysoPC) and higher levels of sphingomyelins have been observed in plasma samples from cancer patients.In addition, three metabolites (LysoPC a C16:0, PC ae C42:5 and PC aa C34:2) differentiate breast cancer patients from healthy controls (39) (Table 1).
Lastly, the bacteria found on the skin have direct access to the breast ducts through the nipple, so the breast tissue contains a wide variety of bacteria, such as Staphylococcus epidermidis and Micrococcus luteus (50, 51).Different species of bacteria perform different functions.For example, Lactobacillus triggers protective mechanisms that include immune activation, competitive inhibition of pathogenic strains, and synthesis of signaling intermediates.In contrast, E. coli and Staphylococcus induce DNA damage (40).Changes in the microbiota of breast and intestinal tissues have been associated with the development of breast cancer (50).Several studies even describe correlations between dysbiosis of the tissue microbiome and the development of breast cancer.When characterizing the microbiome of tumor tissue and adjacent non-tumor tissue from different patients, a higher abundance of taxa belonging to the phylum Actinobacteria was observed in the non-tumor samples.In contrast, Firmicutes and Alpha-Proteobacteria were significantly overrepresented in tumor tissue (52).Healthy individuals show a significantly higher abundance of Lactobacillus, Thermoanaerobacterium thermosaccharolyticum, Candidatus Aquiluna sp., IMCC13023, Anoxybacillus, Leuconostoc, Lactococcus, Geobacillus, Methylobacterium, Turicella otitidis (40), Prevotella, Lactococcus, Streptococcus, Corynebacterium and Micrococcus (41).In cancer patients, there is an abundance of Thermus scotoductus, E. coli, Bacillus cereus, Shewanella, Corynbacterium (40), Bacillus, Staphylococcus, Enterobacteriaceae, Comamondaceae, Bacteroidetes (41), Mycobacterium fortuitum, and Mycobacterium phlei (42).The breast tissue microbiome of women with enrichment in lower abundance taxa, including the genera Fusobacterium, Atopobium, Gluconacetobacter, Hydrogenophaga and Lactobacillus, compared to that of women with benign breast disease (43) (Table 1).
MicroRNAs (miRNAs) are small non-coding RNAs that modulate gene expression to regulate various cellular processes, including those involved in breast cancer (66, 67).miR-206 is highly expressed in ER− tumors and also targets the ERa receptor, as do miR-221 and miR-222 (57).In addition, DCIS and LCIS are characterized by the upregulated expression of several miRNAs, which are listed in Table 2 (57).
Through an integrated study combining genomics and epigenomics, pathways unique to TNBC and non-TNBC were identified.The most significant pathways for TNBC are retinal biosynthesis, BAG2, LXR/RXR, EIF2, and P2Y purinergic receptor signaling pathways, whereas in non-TNBC they are UVB-induced MAPK, PCP, endothelial apelin, endoplasmic reticulum stress, and host viral egress mechanisms (68).Based on the genomic and transcriptomic landscape, Xie et al. have established a new classification of immune subtypes of ER+/PR−/HER2− breast cancer, termed clusters 1 to 5. Cluster 1 is characterized by an activated but suppressive immune microenvironment, immune infiltration, increased stromal content, and an elevated TGF-b response signature.Cluster 2 has an inactivated immune phenotype.Cluster 3 has an activated immune phenotype enriched in innate, adaptive, and immunosuppressive cells, as well as interferon (IFN)-g response, inflammation, macrophage upregulation, and cytolytic signatures.Cluster 4 is characterized by an immunologically inactive phenotype and low infiltration of the microenvironment.Finally, cluster 5 lacks immunologic properties but presents a phenotype associated with hormonal responses (69) (Table 2).
Several studies have investigated the classification of breast cancers using proteomic technologies.By analyzing differential protein expression in tissues, they identified the expression of proteins that characterize luminal subtypes, HER2-enriched breast cancers, and TNBC.In addition, proteomic profiling of these three breast cancer types revealed functional differences.Cytoskeletal remodeling as well as alterations in the cell adhesion process are found in all BC types.Luminal tumors are characterized by increased "energy metabolism" as indicated by an elevated expression of key proteins in gluconeogenesis, electron transport chain and ATP synthase complex."Immune response" is altered in both luminal A and luminal B subtypes, while "cell cycle regulation" is important in luminal B tumors.On the other hand, luminal tumors show decreased expression of proteins related to metabolic pathways, including glycolysis, serine synthesis, and glutamine consumption.The HER2-enriched subtype is characterized by decreased "amino acid and energy metabolism", reduced "cellular community" and increased "glycan biosynthesis and metabolism".Finally, the TNBC subtype is characterized by increased "replication and repair", "cell growth and death" and "translation" pathways.Other relevant processes are "immune response" and "blood coagulation".The most relevant proteins involved in these pathways are listed in Table 2 (58,59,70).Besides that, the expression of the protein enhancer of zeste homolog 2 (EZH2) is elevated in premalignant atypical ductal hyperplasia (ADH) and even higher in DCIS compared to normal epithelium (15).Luminal tumors have been classified into 3 proteomic clusters.Luminal cluster-1 is enriched for RNA processing and splicing processes but depleted for immune-related proteins including the ones involved in antigen processing and presentation, and type I and type II IFN signaling.Luminal cluster-2 is enriched for stromal proteins and extracellular matrix (ECM) components.Luminal cluster-3 has high expression of proteins for DNA replication, cell cycle, response to DNA damage, and immune response, while depleted for ECM components, blood coagulation, epithelial cell differentiation, and response to estrogen and steroid hormones compared to luminal clusters-1 and -2.In addition, significantly higher expression of Ki67 is found in luminal cluster-3 compared to luminal cluster-1 and -2 (71).On the other hand, 4 TNBC subgroups have been identified according to 4 proteomic clusters.TNBC cluster-1 has the most favorable survival and is characterized by immune response, antigen processing and presentation, and IFN type I and II signaling processes.TNBC cluster-2 has intermediate survival and is enriched for ECM components, coagulation, and humoral immune response processes.TNBC cluster-3 has intermediate survival and is enriched for lipid metabolism, catabolism, and oxidation-reduction processes.TNBC cluster-4 exhibits the poorest survival and is enriched for DNA replication and cell cycle proteins (71).
A comprehensive genomic, transcriptomic and proteomic analysis of patients with ILC has identified mutations in cadherin-1 (CDH1) and the phosphatidylinositol 3-kinase (PI3K) pathway as the most common molecular alterations in ILC.In addition, two major subtypes of ILC have been identified: an immune-related (IR) subtype and a hormone-related (HR) subtype: The IR subtype is characterized by upregulation of PD-L1 mRNA, PD-1 and CTLA-4, increased sensitivity to DNA damaging agents, and upregulation of lymphoid signaling molecules at the mRNA level (TGFBR2, IL11RA, TNFRSF17, CCL15, CCL14, CCR2, CD27, XCL2, IFNAR2, and CD40LG).In addition, the IR subtype shows upregulated genes in the cytokinecytokine receptor interaction pathway, suggesting alterations in the composition or functional activity of immune cells within these tumors.Interestingly, the negative regulators of the immune response PDCD1 (PD-1), CD274 (PD-L1) and CTLA4 are expressed at higher mRNA levels in the IR subtype.The related HR subtype is associated with epithelial-to-mesenchymal transition (EMT).Moreover, the HR subtype shows higher levels of estrogen receptors (ESR1) and progesterone receptors (PGR) and upregulation of cell cycle genes and estrogen receptor (ER) target genes (60) (Table 2).
The lipidomic profiles of invasive breast cancer (IBC), DCIS and benign surrounding tissue (BAT) have been investigated.IBC is characterized by the presence of polyunsaturated fatty acids, deprotonated glycerophospholipids and sphingolipids.IBC can be distinguished from BAT by the presence of highly saturated lipids and antioxidant molecules.DCIS differs from IBC by lipids associated with cell signaling and apoptosis (61).Lipidomics have allowed the identification of glycerol-3-phosphate acyltransferase (GPAM), an enzyme involved in triacylglycerol and phospholipid biosynthesis, which shows increased cytoplasmic expression in HRassociated breast cancer and improved OS (63) (Table 2).
Finally, Benarjee et al. have identified a local microbial signature associated with each type of breast tumor.Actinomyces, Bartonella, Brevundimonas, Coxiella, Mobiluncus, Mycobacterium, Rickettsia, and Sphingomonas are common in all types of breast cancer.In the luminal A subtype, Arcanobacterium, Bifidobacterium, Cardiobacterium, Citrobacter, and Escherichia are detected.Bordetella, Campylobacter, Chlamydia, Chlamydophila, Legionella, and Pasteurella are associated with the luminal B subtype.The HER2-enriched subtype is characterized by the presence of Streptococcus, whereas Aerococcus, Arcobacter, Geobacillus, Orientia, and Rothia are associated to with TNBC (65) (Table 2).

Prognostic biomarkers
The various omics technologies have allowed the identification of prognostic biomarkers in both solid and liquid samples.Gene expression analysis have identified genes associated with good and poor prognosis in breast cancer (18).In addition, using data from breast cancer databases (TCGA-BRCA and CMI-MBC), a 45-gene optimal prognostic gene signature has been constructed from genes regulated by tumor-associated macrophages (TAMs).All these genes are listed in Table 3 (75).
Not only genomic but also epigenomic differences have been found.By applying a powerful integrative network algorithm to paired DNA methylation and RNA-Seq data from ER+ breast cancer and adjacent healthy tissue, it has been shown that increased levels of DNA methylation and alterations in mRNA expression can predict poor prognosis.In particular, epigenetic silencing of WNT signaling antagonists and bone morphogenetic proteins (BMPs) has been observed in both luminal subtypes, but predominantly in luminal B breast cancer (76).Davalos et al. identified hypermethylation of RASSF1A and PITX2 associated with poor prognosis in early stage of breast cancer (77).
Various proteomic analyses have validated proteins related to apoptosis, cell cycle arrest, cell adhesion, cytokeratins, cell metabolism and lipid binding as prognostic biomarkers of breast c a n c e r O S ( 7 8 , 7 9 ) .E l e v a t e d e x p r e s s i o n o f s e r i n e hydroxymethyltransferase 2 (SHMT2) correlates with poor OS and relapse-free survival (RFS), the amino acid transporter ASCT2 (SLC1A5) correlates with poor RFS (80), endoplasmin (HSP90B1) has been associated with distant metastasis and worse OS, and decorin (DCN) has been associated with lymph node metastasis, increased number of positive lymph nodes and worse OS; and (81).Finally, increased levels of E-cadherin and b-catenin correlate with poor survival in invasive breast cancer but not in lobular carcinoma (82) (Table 3).
An analysis of breast cancer transcriptomic and proteomic data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC)  3).

Omics data on the onset of breast cancer
Early-stage breast cancer lesions are so small that there may be insufficient material for analysis and it is difficult to obtain accurate data.
The onset of breast cancer is characterized by abnormal paracrine and autocrine signaling, as genes that are highly expressed in healthy breast epithelium are lost in carcinomas, including genes encoding cytokines such as LIF, IL-6, and HIN-1, also known as SCGB3A1, and chemokines such as IL-8, GROa, GROb, and MIP3a, also known as CCL20 (85).In addition, genes silenced by hypermethylation have been identified as responsible for mammary carcinogenesis, including TWIST, RASSF1A, CCND2, HIN1, BRCA1, APC, GSTP1, BIN1, BMP6, ESR2, CDKN2A, CDKN1A, TIMP3 and CST6, as well as the WNT-negative regulators WIF1 and DKK3 (77).In addition, two methylated modifications (H3K9 me2 and me3) of the DNA packaging protein histone H3 decrease during cancer transformation, and the demethylase KDM3A/JMJD1A gradually increases (86).On the other hand, methylated genes such as ITIH5, DKK3, RASSF1A, SFN, CDKN2A, MLH1, HOXD13 and PCDHGB7 have been proposed as potential markers for early detection of breast cancer.Hypermethylation of RASSF1A, CCDN2, HIN1 and APC correlates mainly with HR+ breast cancer, whereas hypermethylation of CDH1 and CDH13 is more frequent in TNBC patients (77).Furthermore, the expression of olfactomedin-4 (OLFM4) is higher in non-invasive breast tumors than in invasive breast cancer.Therefore, OLFM4 may also be a biomarker for early breast cancer (87) (Figure 2A).
Alterations in circulating metabolites have been identified in premenopausal women that may predict the development of breast cancer.In fact, ten metabolites listed in Figure 2A have been associated with breast cancer risk (88).Metabolomic signatures of the taurine and hypotaurine pathways and the alanine, aspartate and glutamate pathways obtained from the plasma of breast cancer patients are critical for early diagnosis (89).In addition, six metabolites and eight metabolic pathways have been identified in blood samples that can be used in the early diagnosis of breast cancer.Of the six metabolites, ethyl (R)-3-hydroxyhexanoate, caprylic acid, and hypoxanthine are noteworthy.Of the eight metabolic pathways identified, fatty acid and aminoacyl-tRNA biosynthesis and inositol phosphate metabolism are the pathways most implicated in the early diagnosis of BC (90).Alterations in lipid metabolism favor processes such as growth, proliferation, and motility of cancer cells, favoring tumor progression (91).The detection of lipids in human plasma samples has led to the identification of diagnostic biomarkers that reflect the early stage of TNBC (ES-TNBC).Diacylglycerol (DG) 34:2 is significantly downregulated in the TNBC subtype.Furthermore, a panel of 5 lipids (DG 34:2, PC 40:3, PC 39:8, PC 34:0 and PC 38:9) can differentiate TNBC from non-TNBC and ES-TNBC from ES-non-TNBC.Finally, in TNBC, ceramides are upregulated, whereas DG and LysoPC are downregulated and PC fluctuates (92) (Figure 2A).

Omics data on the progression of breast cancer
Genomic evidence suggests that IDC is a consequence of DCIS progression.Genes and/or signaling pathways are altered during tumor progression.The most significant changes occur during the transition from normal tissue to carcinoma in situ.In addition to the loss of LIF, IL-6, HIN-1, IL-8, GROa, GROb and MIP3a in carcinomas, glutamine synthase (GS) and desmoplakin (DSP) are the only two genes specifically upregulated in DCIS, while the metabolic enzymes 3-phosphoglycerate dehydrogenase and glyceraldehyde dehydrogenase and mitochondrial NADH: Ubiquinone dehydrogenase and NADH dehydrogenase 1a, have been observed in invasive carcinomas (85).Videlicet, cancer cells have important metabolic alterations (Figure 2B).Downregulated genes in DCIS include TM4SF1, NFKB1A, PBEF, RASD1, TNFRSF10B, TNFAIP, CLU, NSEP1, LITAF/PIG7, BZW1, and CCNL1, as well as genes encoding several cytokines and chemokines such as ILF2, IL13RA1, LIF, CLC, CCL2, and CXCL1.Some transcripts are frequently overexpressed in the DCIS such as PKD1-like, STARD10, EPS8L2, and KIAA0545.Some of these genes are associated with nuclear factor kappa-light-chain-enhancer of activated B cells (NFkB) and tumor necrosis factor (TNF) pathways, resulting in impaired apoptosis and sustained proliferation of breast cancer cells (93).In IDC, upregulated genes can be grouped into genes related to cell cycle, extracellular matrix or secreted proteins, cell adhesion and motility, and signal transduction.Several underexpressed genes have also been detected in IDC, such as TM4SF1, TRAF4, PPARBP, AKR1A1, RSRP1, MAP1LC3A and RBBP6 (93).As for ILCs, they are characterized by CDH1 alterations, as well as dysregulation in the PI3K/Akt pathway due to mutations in PIK3CA, PTEN alterations, and mutations in AKT1 (94) (Figure 2B).
Transcriptomic analysis of tumor cells and their corresponding adjacent microenvironment cells [belonging to the Molecular Taxonomy of Breast Cancer International Consortium "METABRIC" cohort (95)] have shown that CDH1 mutation may deregulate immune cells in the tumor microenvironment (96).Expression of genes encoding the a and b subunits of the integrins ITGA4, ITGB2, ITGAX, ITGB7, ITGAM, ITGAL and ITGA8 correlates positively with the presence of immune cell infiltrates in the tumor, with markers of T cell activation and antigen presentation, and with immunosurveillance gene signatures.Expression of these integrins indicates a favorable prognosis in TNBC and HER2-enriched breast cancers.In contrast, expression of IBSP, ITGB3BP, ITGB6, ITGB1 and ITGAV predict a poor outcome (97) (Figure 2B).
Epigenetic modifications also contribute to breast cancer progression.For example, DNA methylation causes transcriptional silencing of tumor suppressor genes such as RASSF1A, RARB, SFN and TGM2 (98).Genome-wide m e t h y l a t i o n a n a l y s i s r e v e a l e d t h at C p G s it e s w e r e hypermethylated and hypomethylated after CRY2 silencing.These data suggest that the absence of CRY2 causes epigenetic dysregulation of genes leading to breast cancer progression (99).The differences in miRNA expression profile are greater in IDC than in ILC compared to their respective carcinomas in situ.Thus, IDC is characterized by upregulation of let-7d, miR-210 and miR-221 and downregulation of miR-10b, miR-126, miR-143, miR-218, and miR-335-5p.In contrast, ILC is characterized by upregulation of miR-9, miR-375, miR-182 and miR-183 (57) (Figure 2B).
Proteomic analysis of plasma samples from hereditary BC patients carrying a mutation in the BRCA1 gene has shown that gelsolin, whose loss negatively correlates with tumor progression, is downregulated in these samples.In addition, its levels are associated with BRCA1 mutational status (100) (Figure 2B).Furthermore, O-GlcNAcylation is increased in primary malignant breast tumors, and this increase is associated with increased expression of O-GlcNAc transferase in grades II and III breast tumors (31).
Microbiota dysbiosis contributes to breast cancer progression through its effects on skin and breast tissue.The presence of Listeria fleischmannii in breast tumor tissue is associated with epithelialmesenchymal transition (EMT), whereas Haemophilus influenza correlates with tumor growth, cell cycle progression, E2F signaling, and mitotic spindle assembly (51).Alterations in the gut microbiome alter the production of bacterial metabolites that may influence tumor progression in breast cancer.Uric acid, glycolic acid, d-mannitol, 2,3-butanediol and trans-ferulic acid exert cytostatic effects, while 3-hydroxyphenylacetic acid, 4hydroxybenzoic acid and vanillic acid stimulate breast cancer proliferation in vitro.In addition, 3-hydroxyphenylacetic acid, 4hydroxybenzoic acid, 2,3-butanediol and hydrocinnamic acid inhibit EMT, and 2,3-butanediol has both cytostatic and anti-EMT properties (103).Gut microbiota may affect tumor progression by influencing the cancer-immunity dialogue.Gut bacteria elicit a complex and coordinated set of innate and adaptive immune responses to maintain tissue homeostasis.Consequently, when the microbiota-host balance is disrupted and dysbiosis occurs, increased production of inflammatory mediators is observed, which is associated with cancer progression.Low diversity in the gut microbiome is associated with decreased lymphocyte level and increased number of neutrophils, as well as decreased survival in breast cancer patients (51) (Figure 2B).

Omics data in relapsed and/or metastatic breast cancer
Between 7% and 11% of early breast cancers recur locally within 5 years after treatment, and 20% to 30% of primary breast cancers develop distant metastases.According to a study based on Surveillance, Epidemiology, and End Results Program (SEER) data for 2010-2013, the most common sites of breast cancer metastasis are bone (30-60%), lung (21-32%), liver (15-32%), and brain (4-10%) (104).Different molecular subtypes of breast cancer are associated with organ-specific metastases.Thus, luminal subtypes A and B metastasize primarily to the bone, the HER2enriched subtype to the brain and liver, and TNBC to the lung (105).Genomic analyses have shown that metastases retain the same molecular subtype and prognostic signature as their primary tumors.These data suggest that metastatic potential is already determined in the primary tumor.The poor prognostic signature consists of genes that regulate cell cycle, invasion, metastasis, and angiogenesis (106, 107).Common alterations in TP53 (51%) and PIK3CA (49%), as well as mutations/deletions in NF1 (15%), mutations in PTEN (10%), and mutations/deletions in ARID1A (15%) have been identified in metastatic breast cancer tumors (108).Bone metastases of breast cancer are characterized by increased expression of the TFF1, TFF3, AGR2, NAT1 and CR1P1 genes, as well as the chemokine receptors CXCR4 and C-C chemokine receptor type 7 (CCR7), and upregulation of the zinc finger protein SNAI1 (SNAI1) (45,109), which is involved in the induction of EMT (110).In breast to lung cancer metastasis, a number of genes such as MMP1, MMP2, CXCL1, PTGS2, ID1, VCAM1, EREG, SPARC, and IL13RA2 have been identified in breast to lung cancer metastasis that promote and are clinically correlated; as well as the mitogen-activated protein kinases (MAPK), NFkB and vascular endothelial growth factor (VEGF) signaling pathways (111) (45).Mutations in the ESR1, AKT1, ERBB2, and FGFR4 genes have been observed in metastatic breast tumors in the liver (45,112,113).Transcriptome analysis revealed that the TNF-a pathway is upregulated in lung metastases compared to liver metastases (114).ATAD2, DERL1 and NEK2A have been shown to be overexpressed (115-117), whereas ATM, CRYAB and HSPB2 genes are often suppressed and/or underexpressed in breast cancer metastases to the brain (45,118,119), (Figure 2C).The AURORA study, consisting of genomic and transcriptomic profiling in matched primary tumors and early metastases, has described the key role of somatic mutations GATA1 and MEN1 in metastasis.In addition, the enrichment of ESR1, PTEN, and PIK3CA in metastases have been determined, as well as CDH1 and RB1 mutations, MDM4 and MYC amplifications, and ARID1A deletions (120).Of note, TP53 mutations and MYC amplification are associated with shorter time to relapse (25).Eight common genes have been found to have significant effects on TNBC survival (ELOB, SLC39A7, TIMM13, BANF1, NDUFS1, NDUFB7, TRAPPC5, and MVD).Finally, a signature of 87 immune genes has been established that is highly predictive of pathologic complete response (pCR), which in turn correlates with improved OS and distant metastasis-free survival (DMFS) (121).These 87 immune genes are shown in Figure 2C.
The immune system is involved in the development of cancer, from tumor initiation to metastasis (122).Patients with stage 3 and 4 breast cancer have a higher percentage of immunosuppressive cells (granulocytic myeloid-derived suppressor cells (MDSCs), CD14 + CD16 + intermediate monocytes, and CD127 − CD25 high FoxP3 + Treg cells).Inflammation-related genes are differentially expressed in TNBC.In fact, low expression of CD163 and CXCR4 together with high expression of thrombospondin 1 (THBS1) correlates with an increased risk of relapse and poor survival in TNBC (123).The proinflammatory cytokines serum amyloid A (SAA) and IL-18 are elevated in the serum of patients with recurrent breast cancer.Therefore, SAA and IL-18 may be prognostic markers for breast cancer recurrence (124) (Figure 2C).miRNAs are also involved in cancer migration and metastasis.miR-21, miR-10b, miR-373 and miR-520c promote metastasis, whereas miR-126, miR-335, miR-31, miR-146a, and miR-497 suppress metastasis.miR-9 is associated with local recurrence and ER+ tumors, whereas miR-10 is involved in cell proliferation, migration, and invasion (57).On the other hand, Cyclin D2, RARbeta, Twist, RASSF1A and HIN-1 genes show increased methylation in bone, brain and lung metastases compared to primary breast cancer, with HIN-1 and RAR-beta methylation significantly higher in each group (125).Hypermethylation and downregulation of genes involved in breast cancer progression or EMT, such as LYN, MMP7, KLK10 and WNT6, are associated with a significantly lower risk of metastatic relapse (126).Furthermore, H3K4 acetylation has been correlated with breast cancer progression, estrogen responsiveness, and the oncogenic EMT pathway.Therefore, H3K4 is a potential biomarker for tumor progression leading to aggressive metastatic phenotypes (127) (Figure 2C).
Other biomarkers have been identified using other omics techniques.As mentioned above, E-cadherin is considered a good prognostic marker in non-invasive breast cancer, and loss of Ecadherin protein is one of the main features of EMT (82).Furthermore, a metabolomic study has identified 9-cis-retinoic acid as a critical metabolite in breast cancer progression, as it significantly decreased during breast cancer progression to metastasis.This suggests that 9-cis-retinoic acid inhibits tumor progression to metastasis, probably by attenuating cell invasion and migration (128).Combining lipidomic techniques with transcriptomic analysis, PI (18:0/20:3) accumulation has been found to be associated with an increased incidence of lymph node metastasis and activation of the PD-1-related immune checkpoint pathway (129).
Finally, alterations in the microbiota have also been observed to influence breast cancer metastasis.The presence of Bacteroides fragilis, a gut-colonizing bacterium, can induce epithelial hyperplasia to promote tumor growth and metastasis via the bcatenin-Notch1 axis (130).In addition, Fusobacterium nucleatum promotes tumor progression and metastasis (131) (Figure 2C).

Omics and treatment implications
Molecular changes that occur during cancer treatment determine the response to different therapies and guide the optimal choice of treatments to reduce local recurrence and distant metastasis, thereby increasing disease-free survival.The various omics are key to identifying these molecular changes.In fact, several clinical trials are based on one or more omics technologies.
Classically, aromatase inhibitors are recommended for patients with ER+ metastatic breast cancer because they suppress estrogen production.Trastuzumab and lapatinib are administered to patients with HER2-enriched breast tumors, as trastuzumab is a humanized monoclonal antibody against the extracellular domain of HER2, and lapatinib is a tyrosine kinase inhibitor that blocks both HER2 and EGFR activation.However, in light of the omics findings, more refined treatments may be considered according to genomic alterations.For example, both luminal subtypes frequently harbor mutations in PIK3CA, so inhibitors of this kinase may be a therapeutic target; MYC amplification suggests the use of platinum analogs and taxanes; and, patients with BRCA1/2 mutations may benefit from poly ADP ribose polymerase (PARP) inhibitors and/or platinum compounds (44).The RAS/RAF/MEK/ ERK signaling pathway is altered in tumors that overexpress EGFR and HER2.Since this kinase cascade is critical for survival and apoptosis, alterations in this pathway could affect sensitivity/ resistance to anticancer therapies.Mutations in KRAS, HRAS, NRAS, BRAF and NF1 have been observed in all breast cancer subtypes, although their frequency does not exceed 4%.However, copy number alterations (CNAs) of KRAS and BRAF have been observed in TNBC.Therefore, the use of inhibitors of these kinases in combination with other therapies may delay or induce resistance to treatment (132).ESR1 methylation is considered a good predictor of survival in tamoxifen-treated patients, whereas ARHI methylation predicts survival in non-tamoxifen-treated patients.BRCA1 hypermethylation, in turn, sensitizes TNBC patients to DNA-damaging chemotherapeutic agents such as cisplatin and PARP inhibitors (77).The prevalence of the PI3K/AKT/mTOR signaling axis has been observed in TNBC.The c-Kit protein, a receptor tyrosine kinase involved in the initiation of this cascade, is overexpressed in 20-25% of TNBC, hence, the tyrosine kinase inhibitors (TKIs) such as imatinib or sunitinib are used in the treatment of these cancers (133) (Table 4).
Metabolic and lipidomic profiling of TNBC samples combined with transcriptomic and genomic data have identified a number of metabolites as potential therapeutic targets for different transcriptomic subtypes of TNBC.The LAR subtype is characterized by an enrichment of ceramides and fatty acids.Therefore, sphingosine-1-phosphate (S1P), an intermediate of the ceramide pathway, may be a promising drug for the treatment of LAR tumors.In contrast, the basal-like immunosuppressed transcriptomic subtype (BLIS) is characterized by increased metabolites related to oxidation reaction and glycosyl transfer and the lowest level of metabolic dysregulation.In this case, N-acetyl- aspartyl-glutamate has been identified as a key tumor-promoting metabolite and a potential therapeutic target for high-risk BLIS tumors (138).Furthermore, high levels of sphingomyelins are associated with improved DFS in patients with TNBC.Therefore, sphingomyelins and enzymes involved in sphingolipid metabolism could be considered as prognostic markers and potential therapeutic targets, respectively (139).Omics studies can also identify specific pharmacological pathways of resistance and sensitivity of tumor cells to different therapies.For example, a combination of TP53 deficiency and silencing of BRCA1, BRCA2, or BRCA1/2-associated genes results in cisplatin sensitivity (140).
PI3KCA mutations alone or in combination with PTEN appear to predict worse outcome after trastuzumab monotherapy or in combination with chemotherapy (141).BRCA1/2-deficient cells are sensitive to PARP1 inhibitors.In addition, silencing of kinases such as cyclin-dependent kinase 5 (CDK5), mitogen-activated protein kinase 12 (MAPK12), polo-like kinase 3 (PLK3), polynucleotide phosphatase/bifunctional kinase (PNKP), serine/ threonine kinase (STK)22c, and STK36 strongly sensitize to PARP inhibitors (142).Cyclin-dependent kinase (CDK)10 has been identified as a determinant of resistance to endocrine therapies such as tamoxifen, as low CDK10 levels lead to early on tamoxifen treatment (143) (Figure 3A).The classic treatment for HR+ breast cancer is endocrine therapy.However, approximately 30% of patients develop resistance to endocrine therapy.A transcriptomic/proteomic study has identified the set of candidate genes CEACAM1, KRT19, TMEM81, TMEM119, ESRRA, ERBB3, SRC, AKT1S1, SGEF, SCG5, ALOX12B, CKB, BID, XRCC1, NSL1, and CHEK2 that are able to discriminate progression/resistance (PD) from complete response (CR) and correlate significantly with survival (144).In addition, 298 differentially expressed genes were identified between drug-sensitive (DS) and drug-resistant (DR) breast cancer patients prior to neoadjuvant treatment.Among them, the peptidyl-prolyl cis-trans isomerase FKBP4 (FKBP4) and the protein S100-A9 (S100A9) could be putative predictive markers to distinguish the DR group from the DS group of breast cancer patients (145).Metabolic studies have identified biomarkers that predict response to treatment.For example, glycohyocholic and glucodeoxycholic acids can stratify TNBC patients according to response to neoadjuvant chemotherapy and OS (146).On the other hand, when comparing patients with large primary breast cancer who had received neoadjuvant chemotherapy plus bevacizumab with those who had received chemotherapy alone, higher levels of leucine, acetoacetate and trihydroxybutyrate and lower levels of formate were observed 12 weeks after treatment (147).Furthermore, baseline immunometabolic assessment in combination with ER status could predict the response to neoadjuvant targeted chemotherapy (NATC) based on patient trastuzumab-paclitaxel combination and disease relapse in HER2+ patients.HER2+/ER+ patients have higher levels of T-cell stimulating factors, but also higher levels of cytokines that might be responsible for T-cell suppression.The combination of metabolic data with IL-2 and IL-10 cytokine levels has been shown to be prognostic for relapse (148).In addition, serum metabolites such as leucine, formate, valine, and proline, along with hormone receptor status, have been shown to be discriminators of NATC response.For example, formate, proline, valine, HR+, and HER2− are directly associated with NATC resistance.In contrast, leucine, HR− and HER2+ are directly related to NATC sensitivity.In addition, glyoxylate and dicarboxylate metabolism have been implicated in NATC resistance (149) (Figure 3A).
Pathologic complete response (pCR) and residual disease have been correlated with the genome, transcriptome, and tumor immune microenvironment in patients with early and locally advanced breast cancer undergoing neoadjuvant therapy (150).The pCR is associated with overexpression of driver genes such as CDKN2A, EGFR, CCNE1, and MYC and underexpression of CCND1, ZNF703, and ESR1, as well as increased immune activation characterized by enrichment of innate and adaptive immune cells.However, tumors with residual disease, particularly HER2− tumors, showed enrichment of and attenuated immune response due to the enrichment of inhibitory CD56 dim natural killer cells and regulatory T cells, leading to therapy resistance (150).In transcriptomic profiling, HSP90AA1, EEF1A1, APP and HSPA4 were associated with recurrence in breast cancer patients with pCR due to neoadjuvant chemotherapy.TP53, EGFR, CTNNB1, ERBB2 and HSPB1 may play an important role in the survival of pCR patients.Patients with tumors expressing high levels of HSP90AA1, HSPA4, S100A8, and TP53 and low levels of EEF1A1, EGFR and CTNNB1 showed significantly worse overall survival (OS) (151) (Figure 3B).A set of genes including AKT1S1, NSL1, ESRRA, TMEM81, CKB, SGEF, KRT19, SCG5, CEACAM1, ALOX12B, IDB, SRC, CHEK2, ERBB3 and XRCC1 have been identified as critical targets of both selective estrogen receptor modulators (SERMs)/selective estrogen receptor downregulators (SERDs) and aromatase inhibitors (AIs) of endocrine resistance (144).
A systemic metabolic study showed that patients with large primary breast cancers undergoing neoadjuvant chemotherapy with poor response have higher citrate levels and lower histidine levels (147).In addition, TNBC patients with pCR had elevated levels of circulating t-methylhistidine, phenylalanine, p-methylhistidine, lactic acid, glucose, alanine, glutamic acid, citric acid, dimethylamine and phosphocholine, whereas patients with or pathological stable disease (pSD) had elevated levels of valine, 2aminobutanoic acid, propionic acid, ethanol, proline, asparagine, and N,N-dimethylglycine.Finally, TNBC patients with pathologic partial response (pPR) had high levels of 2-hydroxyisovaleric acid, acetoacetate, trimethylamine, creatine, myo-inositol and ornithine, but low levels of five metabolites, namely isoleucine, phenylalanine, threonine, dimethylamine and glycerophosphocholine (Figure 3B).Therefore, we can infer that alterations in the pathways of glycine, serine, and threonine metabolism; valine, leucine, and isoleucine biosynthesis; and alanine, aspartate, and glutamate metabolism could be used as potential models to predict whether a patient with TNBC is suitable to receive neoadjuvant chemotherapy (152).
The relevance of all these biomarkers lies in their translational potential to identify specific treatments for breast cancer.Several clinical trials are ongoing.A Phase I program conducted at the University of Texas MD Anderson Cancer Center demonstrated that patients who received alteration-matched therapy had a higher objective response rate (ORR), PFS, and OS compared to unmatched therapy (153).
One of the most important clinical trials based on genomic studies is the "Microarray In Node Negative Disease may Avoid ChemoTherapy" (MINDACT).The MINDACT clinical trial aims to demonstrate the clinical relevance of the 70-gene prognostic signature (or MammaPrint ™ ) determined by van't Veer (106) and to compare it with the traditional clinicopathologic prognostic indicators for the assignment of adjuvant chemotherapy in patients with node-negative breast cancer (154).As a result, MammaPrint ™ was found to be effective as traditional tools in identifying high-risk patients, but more accurate in identifying low-risk patients who could avoid adjuvant chemotherapy (155,156).This gene signature outperforms all traditional clinical prognostic factors and clearly discriminates patients with an excellent prognosis from those at high risk of recurrence (157,158).Phase III results showed that approximately 46% of women with clinically high-risk breast cancer are unlikely to need chemotherapy (159).Women with high clinical risk and low genomic risk younger than 50 years had excellent DMFS when treated with endocrine therapy alone (160).In addition, patients with ER+, HER2− and stage I lymph node-negative tumors ≤2 cm treated with endocrine therapy had significantly fewer breast cancer events (161).On the other hand, no improvement in outcomes was observed with the use of docetaxel-capecitabine compared with anthracycline-based chemotherapy (162).Last year (2022), the Austrian Group Medical Tumor Therapy prospective registry confirmed that the addition of MammaPrint ™ to the routine treatment of early luminal breast cancer yields clinically useful results (163).
Concurrent with the MINDACT clinical trial in Europe, the Trial Assigning Individualized Treatment Options Rx (TAILORx) was conducted in North America with the goal of reducing chemotherapy overtreatment by integrating molecular diagnostic testing into the clinical decision-making process.TAILORx is based on a 21-gene based assay (Oncotype DX ™ ) that calculates a recurrence score (RS) and reserves chemotherapy for patients with a low RS (164).Women with a high RS who were treated with adjuvant chemotherapy regimens containing taxanes and/or anthracyclines plus endocrine therapy had an estimated 5-year freedom rate from distant breast cancer recurrence of 93% (165).In patients with ER+, HER2−, lymph node-negative and intermediate RS, adjuvant endocrine and chemoendocrine therapy had similar efficacy, although chemotherapy had some benefit in some women aged 50 years or younger (166).Among women with intermediate RS, Hispanic ethnicity and Asian race were associated with better outcomes.However, Black race was associated with worse clinical outcomes and did not benefit from adjuvant chemotherapy (167).
Other studies are the aforementioned AURORA US Metastatasis project and the TransNEO study.The AURORA US Metastasis project conducted a multiomic study including genomics, epigenomics and transcriptomics in primary tumors and their corresponding metastatic breast cancers (168).In metastatic TNBC, significantly lower expression of MHC class I genes (HLA-A, HLA-B, and HLA-C), DNA methylation of HLA-A, and small focal HLA-A were observed, which were associated with lower immunity and worse OS.Tumors with DNA-methylated HLA-A could be targeted for DNA demethylating drugs in combination with immune checkpoint inhibitors (ICI) (168,169).The TransNEO molecular profiling study of patients with early and locally advanced breast cancer undergoing neoadjuvant therapy.Genomic, transcriptomic, and tumor immune microenvironment data were combined with clinical and digital pathology data to perform machine learning to create a predictive pCR model.This model is robust and could guide treatment selection in future clinical trials, including in the context of adjuvant therapy (150).

Conclusion and perspectives
Currently, treatment selection in cancer patients is based on broad clinicopathologic features that fail to accurately classify patients into risk groups, hence resulting into overtreatment of vast segments of patients.Data suggest that ORR, DFS and OS can be improved by the use of tailored therapies.Improved outcomes depend on the identification of new biomarkers that allow for the stratification of patients and eligibility for new therapies.Besides, the identification of biomarkers that predict treatment efficacy will minimize side effects or cumulative toxicity in patients unlikely to benefit from such treatments.Clinical trials, which thus far have mostly been based on genomic, transcriptomic and/or proteomic studies, have been effective in assigning treatment.It is our understanding that multiomics studies, including other omics techniques such as metabolomics, immunomics or microbiomics, are an important step towards precision medicine and hence refine the assignment of the best possible treatment for each patient.Special attention should be paid to the statistical methods used in the analysis of multiomic data to avoid spurious correlations.In fact, correlation coefficients should not be used to explain a process, such as cancer progression, in which multiple variables are involved.In these cases, the use of regression or multivariate analysis techniques will be more appropriate.In addition, validation cohorts will be needed to confirm the reproducibility, robustness, and validity of the results.It is important that validation cohorts have a pre-calculated sample size using statistical power tests and that minimum assay quality criteria have been established.In conclusion, we anticipate that clinical trials based on highdimensional multiomics data interpreted by artificial intelligence will guide each patient to an optimized and personalized treatment, that will avoid overtreatment, minimize side effects, and improve both DFS and OS.

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FIGURE 3Biomarkers of breast cancer defining treatment outcome detected by omics.Biomarkers identified by different omics can determine drug sensitivity/ resistance (A) or type of response to treatment (B).NATC, neoadjuvant targeted therapy; pCR, pathological complete response; pPR, pathological partial response; pSD, pathological stable disease."up arrow" means increase.

TABLE 1
Diagnostic molecular biomarkers of breast cancer detected by different omics.

TABLE 2
Stratification biomarkers of breast cancer detected by different omics.

TABLE 3
Prognostic biomarkers of breast cancer detected by different omics.

TABLE 4
Refined treatments that take into account genomic studies.